Project Summary It is well known that clinical parameters such as clinical stage are correlated with survival outcomes among cancer patients. However, there is much variability among patients, and we are unable to accurately or reliably predict survival for individual patients - a necessary step for personalized cancer medicine. There is a strong need for accurate, reliable outcome predictions for patients, caregivers, and clinical staff. Even the addition of genetic data has yet to make a clinically significant increasein the reliability of our outcome prediction for individual patients. Recent research, including our own, has shown that image features extracted from pre-treatment CT images can be used to predict treatment outcomes for non-small cell lung cancer patients, esophageal cancer patients, and others. A limitation to current studies is the lack of a common platform that would enable research to share results and quickly and easily apply techniques to their own patient datasets. Our proposed project will create open-source software tools that will integrate with current open-source tools that are available for radiation therapy research. We will also carry out an in-depth investigation into the various sources of uncertainty involved in calculating image features, allowing researchers to avoid using features that have high dependence on imaging parameters (such as pixel size). Nearly 100% of NCI- funded clinical trials include pre-treatment CT imaging. Our preliminary work will provide the tools to allow researchers involved in these studies to investigate the use of quantitative image features for predicting treatment outcome.